■ seaborn
Example gallery: https://seaborn.pydata.org/examples/

#====================================================
# ■ plot [matplotlib]
#  matplotlib入門
#    http://bicycle1885.hatenablog.com/entry/2014/02/14/023734
#  Matplotlib: 作図
#    http://www.turbare.net/transl/scipy-lecture-notes/intro/matplotlib/matplotlib.html
#
# 早く知っておきたかったmatplotlibの基礎知識、あるいは見た目の調整が捗るArtistの話
# https://qiita.com/skotaro/items/08dc0b8c5704c94eafb9
# - matplotlibにはグラフを作る際の二つの流儀がある
#
#====================================================

import matplotlib.pyplot as plt
%matplotlib inline

import numpy as np
px = np.arange(-3, 3, 0.1)
py = np.sin(px)

#   a) オブジェクト指向インターフェース
fig, ax = plt.subplots(figsize=(4,3))  #
ax.set_title('Plot')
ax.plot(px,py)

fig = plt.figure(figsize=(4,3))
ax = fig.add_subplot(1,1,1)            # ax <= rows, cols, indicates
ax.set_title('Plot')
ax.plot(px, py)

#   b) plt.なんとかで全部済ませる流儀 (The pyplot API)
plt.figure(figsize=(4,3))
plt.title('Plot')
plt.plot(px, py)
plt.show()

### 重ね描き

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np

px = np.arange(-3, 3, 0.1)
pys = np.sin(px)
pyc = np.cos(px)

plt.figure(figsize=(4,3)) # 描画サイズ、数字はインチ
plt.plot(px, pys, linestyle='-', linewidth=1, marker='+', markersize=6)
plt.plot(px, pyc, linestyle='-', linewidth=1, marker='x', markersize=6)
plt.show()


fig = plt.figure(figsize=(4,3))
ax = fig.add_subplot(1,1,1)            # ax <= rows, cols, indicates
ax.plot(px, pys, linestyle='-', linewidth=1, marker='+', markersize=6)
ax.plot(px, pyc, linestyle='-', linewidth=1, marker='x', markersize=6)

plt.show()

# 散布図
# matplotlib.pyplot.scatter(x, y, s=20, c=None, marker='o', cmap=None, norm=None,
#                           vmin=None, vmax=None, alpha=None, linewidths=None,
#                           verts=None, edgecolors=None, hold=None, data=None,
#                           **kwargs)
# x, y 	グラフに出力するデータ
# s 	サイズ (デフォルト値: 20)
# c 	色、または、連続した色の値
# marker 	マーカーの形 (デフォルト値: ‘o’= 円)
# cmap  	カラーマップ。c が float 型の場合のみ利用可能
# norm  	c を float 型の配列を指定した場合のみ有効。正規化を行う場合の Normalize インスタンスを指定。
# vmin, vmax 	正規化時の最大、最小値。 指定しない場合、データの最大・最小値となります。norm にインスタンスを指定した場合、vmin, vmax の指定は無視されます。
# alpha 	透明度。0(透明)～1(不透明)の間の数値を指定。
# linewidths 	線の太さ
# edgecolors 	線の色
#
# Color Map
#    Example:https://matplotlib.org/examples/color/colormaps_reference.html
from matplotlib import cm

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np

x = np.arange(-3, 3, 0.1)
sy = np.sin(x)
cy = np.cos(x)

fig = plt.figure(figsize=(4,3))
ax = fig.add_subplot(111)
ax.scatter(x, sy, s=5, c='blue')
ax.scatter(x, cy, s=5, c='red')
ax.set_title('scatter plot')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.grid(True)
plt.show()


#-------------
from sklearn import datasets

N = 300  # 全データ数
X, y = datasets.make_moons(N, noise=0.3)
Y = y.reshape(N, 1)
# print(X[0:4])
# print(X[0:4,0])
# print(X[0:4,1])

coldic={0:'red', 1:'blue'}
plt.scatter(X[:,0],X[:,1],c=[coldic[int(y)] for y in Y] )
plt.show()

#=============================
# seaborn
import seaborn as sns

sns.scatterplot(x=X[:,0], y=X[:,1], hue=y, palette='bright',
                marker='o', edgecolor=None, s=20, alpha=0.5)

<AxesSubplot:>

# ◆散布図行列 (Seabone)
# pairplot

# pairplot(data, hue=None, hue_order=None, palette=None, vars=None, x_vars=None, y_vars=None, 
#                kind='scatter', diag_kind='hist', markers=None, height=2.5, aspect=1, dropna=True, 
#                plot_kws=None, diag_kws=None, grid_kws=None)

from sklearn import datasets
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

iris = sns.load_dataset("iris")
#print(iris.head(4))
sns.pairplot(iris, hue="species", height=2.5)
plt.show()

#   sepal_length  sepal_width  petal_length  petal_width species
#0           5.1          3.5           1.4          0.2  setosa
#1           4.9          3.0           1.4          0.2  setosa
#2           4.7          3.2           1.3          0.2  setosa
#3           4.6          3.1           1.5          0.2  setosa

# ◆Seaborn で散布図・回帰モデルを可視化する
#    lmplot, regplot
#        http://pythondatascience.plavox.info/seaborn/%E6%95%A3%E5%B8%83%E5%9B%B3%E3%83%BB%E5%9B%9E%E5%B8%B0%E3%83%A2%E3%83%87%E3%83%AB
#    seaborn.lmplot
#        https://seaborn.pydata.org/generated/seaborn.lmplot.html

# lmplot(x, y, data, hue=None, col=None, row=None, palette=None,
#         col_wrap=None, size=5, aspect=1, markers='o', sharex=True,
#         sharey=True, hue_order=None, col_order=None, row_order=None,
#         legend=True, legend_out=True, x_estimator=None, x_bins=None,
#         x_ci='ci', scatter=True, fit_reg=True, ci=95, n_boot=1000,
#         units=None, order=1, logistic=False, lowess=False, robust=False,
#         logx=False, x_partial=None, y_partial=None, truncate=False,
#         x_jitter=None, y_jitter=None, scatter_kws=None, line_kws=None)

from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns

iris = sns.load_dataset("iris")
sns.lmplot(x="petal_length", y="sepal_length", data=iris, hue="species", 
           markers=["+","x","."], fit_reg=False, legend=True)
plt.show()

# KDE: カーネル密度推定 (kernel density estimate)
from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns

iris = sns.load_dataset("iris")
# Subset the iris dataset by species
setosa     = iris.query("species == 'setosa'")
virginica  = iris.query("species == 'virginica'")
versicolor = iris.query("species == 'versicolor'")

# Set up the figure
f, ax = plt.subplots(figsize=(8, 8))
ax.set_aspect("equal")

# Draw the two density plots
ax = sns.kdeplot(x=setosa.petal_length, y=setosa.sepal_length,
                 cmap="Reds", shade=True, thresh=0.05)
ax = plt.scatter(x=setosa.petal_length, y=setosa.sepal_length,
                 marker="o")

ax = sns.kdeplot(x=virginica.petal_length, y=virginica.sepal_length,
                 cmap="Blues", shade=True, thresh=0.05)
ax = plt.scatter(x=virginica.petal_length, y=virginica.sepal_length,
                 marker="X")

ax = sns.kdeplot(x=versicolor.petal_length, y=versicolor.sepal_length,
                 cmap="Greens", shade=False, thresh=0.05)
ax = plt.scatter(x=versicolor.petal_length, y=versicolor.sepal_length,
                 marker="+")

# Add labels to the plot
red   = sns.color_palette("Reds")[-2]
blue  = sns.color_palette("Blues")[-2]
green = sns.color_palette("Greens")[-2]
ax = plt.text(2.0, 4.5, "setosa",     size=16, color=red)
ax = plt.text(3.5, 7.2, "virginica",  size=16, color=blue)
ax = plt.text(2.5, 6.0, "versicolor", size=16, color=green)
plt.show()

# jointplot, JointGrid
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns

iris = sns.load_dataset("iris")

# グラフの種類をkindで指定
# kind{ “scatter” | “kde” | “hist” | “hex” | “reg” | “resid” }
sns.jointplot(data=iris, x="petal_length", y="sepal_length", hue="species", alpha=0.5,
              kind="scatter") 

# 内側と外側のグラフ種を指定
g = sns.JointGrid(data=iris, x="petal_length", y="sepal_length", hue="species")
g.plot(sns.kdeplot, sns.histplot)

# 内側と外側のグラフ種と各設定で指定
g = sns.JointGrid(data=iris, x="petal_length", y="sepal_length", hue="species")
g.plot_joint(sns.scatterplot, s=100, alpha=.5)
g.plot_marginals(sns.histplot, bins=20, multiple='dodge', kde=True)


#ガウス分布の確率密度関数,平均 0、標準偏差1
rx = np.random.normal(5, 2, 100).round()
ry = 0.5*rx +2

g = sns.JointGrid(x=rx, y=ry)
g.plot_joint(sns.regplot, x_jitter=0.2, y_jitter=0.04, fit_reg=False, 
             scatter_kws={'alpha':0.3, 's':10})
g.plot_marginals(sns.histplot, bins=25, multiple='dodge', kde=False)

<seaborn.axisgrid.JointGrid at 0x1eb5b954a30>

# 目的変数が連続、Featureが連続変数の場合
# Featue毎に、目的関数との相関をプロット
import numpy as np
import pandas as pd
from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

def plt_scatter(target, feature):
    xmargin = ( max(feature) - min(feature) ) * 0.03
    ymargin = ( max(target ) - min(target ) ) * 0.03
    plt.xlim([min(feature)-xmargin, max(feature)+xmargin])
    plt.ylim([min(target )-ymargin, max(target )+ymargin])
    return sns.regplot(x=feature, y=target,
                       scatter_kws={'alpha':0.4, 's':10}, 
                       line_kws={'lw':1, 'color':'black'})

# ボストンデータセット
boston = datasets.load_boston()
df_X   = pd.DataFrame(boston.data, columns=boston.feature_names)
sr_y   = pd.Series(boston.target, name='PRICE')

# データセットの初めからN列をプロット
n_features = 5
for i, feature_name in enumerate(boston.feature_names[0:n_features]):
    print(i, feature_name)
    if i%2+1 <= 1:
        fig = plt.figure(figsize=(8,3))
        ax = plt.subplots_adjust(wspace=0.3)

    ax = fig.add_subplot(1, 2, i%2+1)
    ax = plt_scatter(sr_y, df_X[feature_name])
    
    if i%2+1 >= 2:
        plt.show()
        
plt.show()

0 CRIM
1 ZN

2 INDUS
3 CHAS

4 NOX

# 目的変数が連続、Featureがカテゴリ変数の場合
# Featue毎に、目的関数との相関をプロット
import numpy as np
import pandas as pd
from sklearn import datasets
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

# diamondsデータセット
df = sns.load_dataset('diamonds')
print(df.dtypes)
cat_features = df.dtypes[df.dtypes == 'category'].index

def def_cat_profile(feature):
    n_categories = len(feature.cat.categories)
    return n_categories, feature.value_counts().to_dict()

def plt_dist_box(df, feature):
    fig = plt.figure(figsize=(8,3))
#    ax = plt.subplots_adjust(wspace=0.05)

    # boxplot
    ax0 = plt.subplot2grid((1, 6), (0, 0), colspan=4)
    g = sns.boxplot(y=df[feature], x=df[target], color='#628CD2', width=0.7, 
                    flierprops={'markersize':3,'markerfacecolor':'gray','markeredgecolor':'None','alpha':0.2})
    ax0 = g.set_yticklabels(g.get_yticklabels(), size=font_size)

    # distribution
    ax1 = plt.subplot2grid((1, 6), (0, 4), colspan=2)
    g = sns.countplot(y=df[feature], color='#628CD2')
    ax1 = g.set_yticklabels([])
    ax1 = g.yaxis.set_visible(False)
    plt.show()

print(f'length: {len(cat_features)} Category Features')
target = 'price'
sns.set()
for feature in cat_features:
    font_size = 10 if len(df[feature].cat.categories) < 20 else 8
    print(feature, def_cat_profile(df[feature]))
    plt_dist_box(df, feature)

carat       float64
cut        category
color      category
clarity    category
depth       float64
table       float64
price         int64
x           float64
y           float64
z           float64
dtype: object
length: 3 Category Features
cut (5, {'Ideal': 21551, 'Premium': 13791, 'Very Good': 12082, 'Good': 4906, 'Fair': 1610})

color (7, {'G': 11292, 'E': 9797, 'F': 9542, 'H': 8304, 'D': 6775, 'I': 5422, 'J': 2808})

clarity (8, {'SI1': 13065, 'VS2': 12258, 'SI2': 9194, 'VS1': 8171, 'VVS2': 5066, 'VVS1': 3655, 'IF': 1790, 'I1': 741})

# 目的変数が連続、Featureがカテゴリ変数の場合
# Featue毎に、目的関数との相関、および、回帰直線を求めて残差をプロット

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

def plt_regplot(target, feature):
    xmargin = ( max(feature) - min(feature) ) * 0.03
    ymargin = ( max(target ) - min(target ) ) * 0.03
    plt.xlim([min(feature)-xmargin, max(feature)+xmargin])
    plt.ylim([min(target )-ymargin, max(target )+ymargin])
    return sns.regplot(x=feature, y=target,
                       scatter_kws={'alpha':0.5, 's':12}, 
                       line_kws={'lw':1, 'color':'black'})

boston = datasets.load_boston()
df_X   = pd.DataFrame(boston.data, columns=boston.feature_names)
df_y   = pd.DataFrame(boston.target, columns=['Price'])
df     = pd.concat([df_X,df_y],axis=1)

n_features = 3
print(f'length: {len(boston.feature_names)} Numeric Features')
target = 'Price'
for feature in boston.feature_names[0:3]:
    x = np.array(df[feature]).reshape(-1,1)
    y = np.array(df[target])
    y_hat = LinearRegression().fit(x,y).predict(x) ## 直線回帰
    y_err = y - y_hat                              ## 残差
    idx1 = np.argmax(np.abs(y_err))
    y_err2      = y_err.copy()
    y_err2[idx1] = 0.0
    idx2 = np.argmax(np.abs(y_err2))
    std = np.std(y_err)
    print(f'The candidates of outlier:',
          f'{feature}: {idx1}:{df.loc[idx1, feature]}, {idx2}:{df.loc[idx2, feature]}')

    fig = plt.figure(figsize=(10,3))
    ax = plt.subplots_adjust(wspace=0.3)

    #### Prediction - actual
    ax = fig.add_subplot(1, 2, 1)
    ax = plt_regplot(df[target].astype(np.float64), df[feature].astype(np.float64))
    ax = plt.text(df.loc[idx1, feature], df.loc[idx1, target], str(idx1), 
                  ha='right', va='bottom', color='red')
    ax = plt.text(df.loc[idx2, feature], df.loc[idx2, target], str(idx2), 
                  ha='right', va='bottom', color='red')

    #### residuals
    ax = fig.add_subplot(1, 2, 2)
    ax = plt.hlines( 3*std, np.min(x),np.max(x), ls='solid', lw=1, color='red')
    ax = plt.hlines(-3*std, np.min(x),np.max(x), ls='solid', lw=1, color='red')
    ax = plt.text(np.max(x),3*std, '3 sigma', ha='right', va='top', color='red', size=10)
    ax = plt.text(df.loc[idx1, feature], y_err[idx1], str(idx1), 
                  ha='right', va='bottom', color='red')
    ax = plt.text(df.loc[idx2, feature], y_err[idx2], str(idx2), 
                  ha='right', va='bottom', color='red')
    ax = plt.scatter(x=x, y=y_err, alpha=0.3, s=10)
    ax = plt.hlines(     0, np.min(x),np.max(x), ls='solid', lw=1.5, color='black')

    plt.show()

length: 13 Numeric Features
The candidates of outlier: CRIM: 371:9.2323, 372:8.26725

The candidates of outlier: ZN: 161:0.0, 162:0.0

The candidates of outlier: INDUS: 161:19.58, 162:19.58

# 3次元散布図
#
# http://www.python.ambitious-engineer.com/archives/883
# mplot3d tutorial
# https://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html
# mpl_toolkits.mplot3d.axes3dというライブラリを追加でインポート

import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D

n=20
x = np.arange(n*n)//n
y = np.arange(n*n)%n
#z = (x+y)/2 + (np.random.rand(n*n)-0.5)*2
z = (x+y)/2 + np.sin(x+y)
v = np.sqrt( (x-(n-1)/2)**2 + (y-(n-1)/2)**2 + (z-(n-1)/2)**2 )/n
#print(x,y,z,v)

# 散布図を表示、各点の色にvalueを指定する
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
p = ax.scatter(x, y, z, c=v, cmap='Blues')
  
# カラーバーを表示
fig.colorbar(p, shrink=0.8)
 
plt.show()

# 3次元サーフェス
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np


fig = plt.figure()
ax = fig.add_subplot(projection='3d')

# Make data.
n=20
X = np.arange(n)
Y = np.arange(n)
X, Y = np.meshgrid(X, Y)
Z = (X+Y)/2 + np.sin(X+Y)
#print(X[:3])
#print(Z[:3])

# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
                       linewidth=0, antialiased=False)

# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)

plt.show()

# 2 次元等高線図(コンター図)(塗りつぶし)
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np

xs = np.arange(-5, 5, 1)
z = np.array([x**2 + y**2 for x in xs for y in xs]).reshape((len(xs), len(xs)))
print(xs)
print(z)
plt.contourf(xs, xs, z, cmap=cm.jet)
plt.colorbar()
plt.show()

[-5 -4 -3 -2 -1  0  1  2  3  4]
[[50 41 34 29 26 25 26 29 34 41]
 [41 32 25 20 17 16 17 20 25 32]
 [34 25 18 13 10  9 10 13 18 25]
 [29 20 13  8  5  4  5  8 13 20]
 [26 17 10  5  2  1  2  5 10 17]
 [25 16  9  4  1  0  1  4  9 16]
 [26 17 10  5  2  1  2  5 10 17]
 [29 20 13  8  5  4  5  8 13 20]
 [34 25 18 13 10  9 10 13 18 25]
 [41 32 25 20 17 16 17 20 25 32]]

# 2次元、3次元配列の可視化

import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm

x = np.array([1,1,1,1,1, 2,2,2,2,2, 3,3,3,3,3, 4,4,4,4,4, 5,5,5,5,5])
y = np.array([1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5])
z = np.array([5,6,7,5,2, 3,5,6,7,6, 3,5,4,3,2, 4,5,5,4,2, 4,4,4,3,3])

# 2D Scatter; Heatmap 的なもの
fig = plt.figure(figsize=(4,3))
p = plt.scatter(x, y, c=z, linewidths=0, alpha=.8, cmap=cm.jet)
fig.colorbar(p)
plt.show()

# 塗りつぶし2次元等高線図
print(np.reshape(z,(5,5)).T)
plt.contourf([1,2,3,4,5], [1,2,3,4,5], np.reshape(z,(5,5)).T, cmap=cm.jet)
plt.colorbar()
plt.show()


# 3D plot
# Plot the Scatter.
fig = plt.figure(figsize=(6,5))
ax = fig.add_subplot(projection='3d')
p = ax.scatter(x, y, z, c=z, cmap=cm.jet)
fig.colorbar(p, shrink=0.5)
plt.show()

# Plot the surface.
x = np.arange(1,6)
y = np.arange(1,6)
x, y = np.meshgrid(x, y)
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
surf = ax.plot_surface(x, y, np.reshape(z,(5,5)).T, cmap=cm.jet,
                       linewidth=0, antialiased=False)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()

# 塗りつぶし等高線図の3次元表示
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
p = ax.contourf([1,2,3,4,5], [1,2,3,4,5], np.reshape(z,(5,5)).T, cmap=cm.jet)
fig.colorbar(p, shrink=0.5)
plt.show()

[[5 3 3 4 4]
 [6 5 5 5 4]
 [7 6 4 5 4]
 [5 7 3 4 3]
 [2 6 2 2 3]]

# 2次元ヒストグラム
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

x, y = np.vstack((np.random.multivariate_normal([0, 0], [[10.0, 0],[0,20]], 5000) 
                 ,np.random.multivariate_normal([0,15], [[10.0, 0],[0, 5]], 5000))).T

fig = plt.figure()
ax = fig.add_subplot(111)

H = ax.hist2d(x,y, bins=40, cmap=cm.jet)
ax.set_title('2D Histgram')
ax.set_xlabel('x')
ax.set_ylabel('y')
fig.colorbar(H[3],ax=ax)
plt.show()

# matplotlib でヒストグラムを描く
#    http://pythondatascience.plavox.info/matplotlib/%E3%83%92%E3%82%B9%E3%83%88%E3%82%B0%E3%83%A9%E3%83%A0

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from matplotlib import cm
from numpy.random import *

seed(100)
x = np.random.normal(50, 10, 1000)  # 平均 50, 標準偏差 10 の正規乱数を 1,000件生成
y = np.random.normal(80, 15,  700)  # 平均 80, 標準偏差 15 の正規乱数を   700件生成
z = np.random.normal(5, 1, 1000).astype(int)*10
c = np.random.choice(['A','B','C'], 1000, p=[0.2, 0.5, 0.3]) # p: probabilities 

# matplotlib.pyplot.hist(x, bins=10, range=None, normed=False, weights=None,
#                        cumulative=False, bottom=None, histtype='bar',
#                        align='mid', orientation='vertical', rwidth=None,
#                        log=False, color=None, label=None, stacked=False,
#                        hold=None, data=None, **kwargs)

# ヒストグラムを出力
fig = plt.figure(figsize=(8,3)) ## (w,h)
ax = fig.add_subplot(1, 2, 1)
ax.hist(x, bins=20, ec='black')
ax.set_title('Histogram')
ax.set_xlabel('X')
ax.set_ylabel('y')
ax = fig.add_subplot(1, 2, 2)
ax.hist(x, bins=20, color='skyblue', cumulative=True, ec='blue')
ax.set_title('Cumulative Histogram')
ax.set_xlabel('X')
#fig.show()

fig = plt.figure(figsize=(12,3)) ## (w,h)
# オーバーラップ
ax = fig.add_subplot(1, 3, 1)
ax.hist(x, bins=20, label='X', color='blue', alpha=0.5)
ax.hist(y, bins=20, label='Y', color='red',  alpha=0.5)
ax.legend()

# 積み上げ
ax = fig.add_subplot(1, 3, 2)
ax.hist([x,y], bins=20, label=['X','Y'], color=['blue','red'], stacked=True)
ax.legend()

# 並べ
ax = fig.add_subplot(1, 3, 3)
ax.hist([x,y], bins=20, label=['X','Y'], color=['blue','red'], stacked=False)
ax.legend()
plt.show()

df = pd.DataFrame({'x':x, 'c':c})
# Seaborn
fig, ax = plt.subplots(figsize=(6,3))
sns.histplot(data=df, x='x', hue='c', stat='count', bins=10, multiple='dodge', shrink=0.8, kde=True)
plt.show()

fig, ax = plt.subplots(figsize=(6,3))
sns.countplot(x='z', hue='c', data=pd.DataFrame({'z':z, 'c':c}))
plt.show()

# barh 水平棒グラフに数値コメント
import matplotlib.pyplot as plt

x     = [10,20,15,18]
y     = list(reversed(range(len(x))))
label = ['Aaa','Bbb','Ccc','Ddd']
plt.barh(y, x, align = 'center', xerr=[1,2,3,4], ecolor="black", capsize=10)
for i in y:
    plt.text(x[i], y[i]+0.2, '{:.3f}'.format(x[i]), ha='left', va='center')
plt.yticks(y, label)
plt.show()

# barplot
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame({ 
    'A'  : ['CC','BB','AA','DD'],
    'B1' : [0.92,0.94,0.96,0.98],
    'B2' : [0.95,0.97,0.93,0.99]
})
df = pd.melt(df.sort_values(['B1']),   ## pivot_wider
             id_vars=['A'], value_vars=['B1','B2'],
             var_name='B_key', value_name='B_val')
display(df)

sns.barplot(data=df, x='B_val', y='A', hue='B_key')
plt.xlim(0.9,1.0)
plt.legend(bbox_to_anchor = (1.05, 1), # legendの原点位置をplot枠の外側 
           loc = 'upper left',         # legendの原点位置
           borderaxespad = 0)
plt.show()

	A	B_key	B_val
0	CC	B1	0.92
1	BB	B1	0.94
2	AA	B1	0.96
3	DD	B1	0.98
4	CC	B2	0.95
5	BB	B2	0.97
6	AA	B2	0.93
7	DD	B2	0.99

# カテゴリ変数; BOXプロットのバリエーション
# boxenプロット、ジッター散布図, バイオリンプロット

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
print(tips.head())
fig = plt.figure(figsize=(12,4)) ## (w,h)
ax = fig.add_subplot(1, 2, 1)
ax.set_title('boxplot')
ax = sns.boxplot(data=tips, x="day", y="total_bill", hue="smoker", width=0.8, palette="muted")
ax = fig.add_subplot(1, 2, 2)
ax.set_title('boxenplot')
ax = sns.boxenplot(data=tips, x="day", y="total_bill", hue="smoker", palette="muted")
plt.show()

fig = plt.figure(figsize=(12,4)) ## (w,h)
ax = fig.add_subplot(1, 2, 1)
ax.set_title('stripplot')
sns.stripplot(data=tips, x="day", y="total_bill", hue="smoker", palette="muted", alpha=0.5, dodge=True)
ax = fig.add_subplot(1, 2, 2)
ax.set_title('swarmplot')
sns.swarmplot(data=tips, x="day", y="total_bill", hue="smoker", palette="muted", s=4, dodge=True)
plt.show()

fig = plt.figure(figsize=(12,4)) ## (w,h)
ax = fig.add_subplot(1, 2, 1)
ax.set_title('violinplot')
ax = sns.violinplot(data=tips, x="day", y="total_bill", hue="smoker", palette="muted", split=True)
plt.show()

fig = plt.figure(figsize=(12,4)) ## (w,h)
ax = fig.add_subplot(1, 2, 1)
ax = sns.boxplot(  data=tips, x="day", y="total_bill", hue="smoker", dodge=True, color='white')
ax = sns.stripplot(data=tips, x="day", y="total_bill", hue="smoker", dodge=True, s=6, alpha=0.4)
plt.show()

   total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4

# Pillow (PIL Fork) https://pillow.readthedocs.io/en/stable/reference/Image.html#functions
import numpy as np
import matplotlib.pyplot  as plt
import matplotlib.patches as patches
import matplotlib.collections as mc
%matplotlib inline
from PIL import Image # Pillow

img = Image.open('./Data/photo.jpg')
print('Format:',img.format, 'Size w,h:',img.size, 'Mode:',img.mode)
print(img.getextrema())  # the minimum and maximum pixel values for each band
img = np.array(img,'float32')/255
print('Shape h,w,c:',img.shape)

# Quick plot (The pyplot API)
#plt.imshow(img) # Matplotlib(H, W, CH)
#plt.show()

# The object-oriented API
x1, y1 = 100, 150
x2, y2 = 300, 350

### ec: edgecolor, fc: facecolor, lw: linewidth
r = patches.Rectangle((x1,y1), x2-x1,y2-y1, ec='red', lw=2, fill=False) # 矩形 (x,y), w, h
c = patches.Circle( xy=(300, 200), radius=100, lw=2, ec='blue', fill=False, fc=None ) # 円
e = patches.Ellipse(xy=(350, 400), width=150, height=100, ec='blue', fc='blue', alpha=0.1) # 楕円

# 複数の線分
lines = [[( 50,  80), (100, 120), (150,  50)], 
         [(150,  50), (200, 120)], 
         [(200,  50), (250, 120)]] # [[(x1, y2), (x2, y2), ...], ...]
lc = mc.LineCollection(lines, 
                       colors=['red','orange','limegreen'], 
                       linewidths=[2,3,4], 
                       linestyles=['-','--','-'])

# 複数のポリゴン
polygons = [patches.Polygon([(300, 100), (350,  50), (500,  80), (450, 150)], closed=True), 
            patches.Polygon([(420, 120), (450,  20), (480,  80), (420, 150)], closed=True)]
pc = mc.PatchCollection(polygons, 
                        linewidths =[ 2,3 ],
                        facecolors=['pink','limegreen'  ], alpha=0.3,
                        edgecolors=['red', 'forestgreen'], zorder=2)

fig, ax = plt.subplots(figsize=(10,8))
#fig = plt.figure(figsize=(10,8))
#ax = plt.axes()
ax.grid()
ax.set_axisbelow(True)
ax.imshow(img)
ax.add_patch(r)
ax.add_patch(c)
ax.add_patch(e)
ax.add_collection(lc)
ax.add_collection(pc)
ax.axhline(120, color='yellow', linewidth=3, linestyle=':')   # 水平線
ax.axvline(400, color='yellow', linewidth=3, linestyle='--')  # 垂直線
ax.plot([100, 300], [350,150], color='skyblue')   # 線分 [x1, x2],[y1,y2] 
plt.show()

Format: JPEG Size w,h: (520, 480) Mode: RGB
((0, 255), (0, 255), (0, 255))
Shape h,w,c: (480, 520, 3)

# pillow; 画像の反転(flip)、回転(rotate)

import numpy as np
import matplotlib.pyplot  as plt
%matplotlib inline
from PIL import Image # Pillow

def plt_img(img1, title1=None, 
            img2=None, title2=None, img3=None, title3=None, img4=None, title4=None):
    n_img = 1
    if not ((img2 is None) or (title2 is None)):
        n_img = 2
    if not ((img3 is None) or (title3 is None)):
        n_img = 3
    if not ((img4 is None) or (title4 is None)):
        n_img = 4    
    
    fig = plt.figure(figsize=(16,6))

    ax = fig.add_subplot(1, n_img, 1)
    ax.set_title(title1)
    ax.imshow(img1)
    ax.grid()
    if not ((img2 is None) or (title2 is None)):
        ax = fig.add_subplot(1, n_img, 2)
        ax.set_title(title2)
        ax.imshow(img2)
        ax.grid()
    if not ((img3 is None) or (title3 is None)):
        ax = fig.add_subplot(1, n_img, 3)
        ax.set_title(title3)
        ax.imshow(img3)
        ax.grid()
    if not ((img4 is None) or (title4 is None)):
        ax = fig.add_subplot(1, n_img, 4)
        ax.set_title(title4)
        ax.imshow(img4)
        ax.grid()

    plt.show()

img = Image.open('./Data/photo.jpg')
print('Format:',img.format, 'Size w,h:',img.size, 'Mode:',img.mode)
print(img.getextrema())  # the minimum and maximum pixel values for each band
img_arr = np.array(img,'float32')/255
print('Shape h,w,c:',img_arr.shape)

plt_img(img_arr,'./Data/photo.jpg',  np.fliplr(img_arr),'fliplr',
        np.rot90(img_arr,1),'rot90', np.rot90(img_arr,3),'rot90') # rotate unticlockwise

Format: JPEG Size w,h: (520, 480) Mode: RGB
((0, 255), (0, 255), (0, 255))
Shape h,w,c: (480, 520, 3)

# cv2で読み込んだarrayをNotebookに表示

import cv2
from IPython import display

def imshow(img, format=".jpg", **kwargs):
    """ndarray 配列をインラインで Notebook 上に表示する。
    """
    img = cv2.imencode(format, img)[1]    ### <= format (.jpg)に合わせて変換する
    img = display.Image(img, **kwargs)
    display.display(img)


img = cv2.imread('./Data/photo.jpg')
imshow(img)

# ペアプロット、相関係数ヒートマップ、散布図+回帰直線、残差プロット

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import cm
pd.set_option('display.width',     100)
pd.set_option('display.precision',   4)

# Wine Data
wine = pd.read_csv('./data/winequality-red.csv', sep=';',  header=0)
print('Describe:\n', wine.describe(percentiles=[.5]), sep='')
sns.pairplot(wine, height=2.5)
plt.show()

wine_feature_names = list(wine.columns)
wine_feature_names.remove('quality')

# 各Featureを正規化(0～1)
wine_nr = wine
wine_nr[wine_feature_names] = wine_nr[wine_feature_names].apply(
                                  lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))

# 相関係数
print('\n相関係数を表示\n', wine_nr.corr())
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
ax.set_title('Pearson Correlation Coefficient')
sns.heatmap(wine_nr.corr(), annot=True, fmt='.2f', center=0, square=False, cmap=cm.seismic)
plt.yticks(rotation='horizontal')
plt.show()

# 各Featureとqualityとの相関をプロット
wine_nr_lf = pd.melt(wine_nr, id_vars=['quality'], value_vars=wine_feature_names)
print('Tidied\n', wine_nr_lf.head(4))
sns.lmplot(x='value', y='quality', data=wine_nr_lf, hue='variable', col='variable',
           scatter_kws={'s':3}, y_jitter=0.02, col_wrap=4, height=2.5)
plt.show()

# 説明変数に "quality (品質スコア)以外すべて" を利用
X = wine_nr.drop("quality", axis=1).values
# 目的変数に "quality (品質スコア)" を利用
Y = wine['quality'].values
 
# 予測モデルを作成
from sklearn import linear_model
clf = linear_model.LinearRegression()
clf.fit(X, Y)
 
# 偏回帰係数
print(pd.DataFrame({"Name":wine_feature_names,
                    "Coefficients":clf.coef_}).sort_values(by='Coefficients', ascending=False) )
# 切片 (誤差)
print('Intercept: ', clf.intercept_)

## 残差分析
# 予測値
predictions = clf.predict(X)
# 残差
residuals   = predictions - wine['quality']
# 残差の標準偏差
std         =  np.std(residuals)
print('Standard deviation of Residuals:',std)

plt.scatter(x=wine.index, y=residuals, s=3, c='blue', marker='.')
plt.xlabel('Index')
plt.ylabel('Residuals')
# y = 0に直線を引く
plt.hlines(y=0,    xmin=0, xmax=len(predictions), lw=2, color='red')
plt.hlines(y= std, xmin=0, xmax=len(predictions), lw=1, color='green')
plt.hlines(y=-std, xmin=0, xmax=len(predictions), lw=1, color='green')
plt.show()

wine_nr_rs = wine_nr
wine_nr_rs['residuals'] = residuals
wine_nr_rs_lf = pd.melt(wine_nr_rs, id_vars=['residuals'], value_vars=wine_feature_names)
print('Tidied\n', wine_nr_rs_lf.head(4))
sns.lmplot(x='value', y='residuals', data=wine_nr_rs_lf, hue='variable', col='variable',
           scatter_kws={'s':3}, col_wrap=4, height=2.5)
plt.show()

Describe:
       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \
count      1599.0000         1599.0000    1599.0000       1599.0000  1599.0000   
mean          8.3196            0.5278       0.2710          2.5388     0.0875   
std           1.7411            0.1791       0.1948          1.4099     0.0471   
min           4.6000            0.1200       0.0000          0.9000     0.0120   
50%           7.9000            0.5200       0.2600          2.2000     0.0790   
max          15.9000            1.5800       1.0000         15.5000     0.6110   

       free sulfur dioxide  total sulfur dioxide    density         pH  sulphates    alcohol  \
count            1599.0000             1599.0000  1599.0000  1599.0000  1599.0000  1599.0000   
mean               15.8749               46.4678     0.9967     3.3111     0.6581    10.4230   
std                10.4602               32.8953     0.0019     0.1544     0.1695     1.0657   
min                 1.0000                6.0000     0.9901     2.7400     0.3300     8.4000   
50%                14.0000               38.0000     0.9968     3.3100     0.6200    10.2000   
max                72.0000              289.0000     1.0037     4.0100     2.0000    14.9000   

         quality  
count  1599.0000  
mean      5.6360  
std       0.8076  
min       3.0000  
50%       6.0000  
max       8.0000  

相関係数を表示
                       fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \
fixed acidity                1.0000           -0.2561       0.6717          0.1148     0.0937   
volatile acidity            -0.2561            1.0000      -0.5525          0.0019     0.0613   
citric acid                  0.6717           -0.5525       1.0000          0.1436     0.2038   
residual sugar               0.1148            0.0019       0.1436          1.0000     0.0556   
chlorides                    0.0937            0.0613       0.2038          0.0556     1.0000   
free sulfur dioxide         -0.1538           -0.0105      -0.0610          0.1870     0.0056   
total sulfur dioxide        -0.1132            0.0765       0.0355          0.2030     0.0474   
density                      0.6680            0.0220       0.3649          0.3553     0.2006   
pH                          -0.6830            0.2349      -0.5419         -0.0857    -0.2650   
sulphates                    0.1830           -0.2610       0.3128          0.0055     0.3713   
alcohol                     -0.0617           -0.2023       0.1099          0.0421    -0.2211   
quality                      0.1241           -0.3906       0.2264          0.0137    -0.1289   

                      free sulfur dioxide  total sulfur dioxide  density      pH  sulphates  \
fixed acidity                     -0.1538               -0.1132   0.6680 -0.6830     0.1830   
volatile acidity                  -0.0105                0.0765   0.0220  0.2349    -0.2610   
citric acid                       -0.0610                0.0355   0.3649 -0.5419     0.3128   
residual sugar                     0.1870                0.2030   0.3553 -0.0857     0.0055   
chlorides                          0.0056                0.0474   0.2006 -0.2650     0.3713   
free sulfur dioxide                1.0000                0.6677  -0.0219  0.0704     0.0517   
total sulfur dioxide               0.6677                1.0000   0.0713 -0.0665     0.0429   
density                           -0.0219                0.0713   1.0000 -0.3417     0.1485   
pH                                 0.0704               -0.0665  -0.3417  1.0000    -0.1966   
sulphates                          0.0517                0.0429   0.1485 -0.1966     1.0000   
alcohol                           -0.0694               -0.2057  -0.4962  0.2056     0.0936   
quality                           -0.0507               -0.1851  -0.1749 -0.0577     0.2514   

                      alcohol  quality  
fixed acidity         -0.0617   0.1241  
volatile acidity      -0.2023  -0.3906  
citric acid            0.1099   0.2264  
residual sugar         0.0421   0.0137  
chlorides             -0.2211  -0.1289  
free sulfur dioxide   -0.0694  -0.0507  
total sulfur dioxide  -0.2057  -0.1851  
density               -0.4962  -0.1749  
pH                     0.2056  -0.0577  
sulphates              0.0936   0.2514  
alcohol                1.0000   0.4762  
quality                0.4762   1.0000  

Tidied
    quality       variable   value
0        5  fixed acidity  0.2478
1        5  fixed acidity  0.2832
2        5  fixed acidity  0.2832
3        6  fixed acidity  0.5841

                    Name  Coefficients
10               alcohol        1.7953
9              sulphates        1.5303
5    free sulfur dioxide        0.3097
0          fixed acidity        0.2824
3         residual sugar        0.2384
2            citric acid       -0.1826
7                density       -0.2435
8                     pH       -0.5253
6   total sulfur dioxide       -0.9239
4              chlorides       -1.1227
1       volatile acidity       -1.5820
Intercept:  5.71255299667039
Standard deviation of Residuals: 0.6455750670692051

Tidied
    residuals       variable   value
0     0.0329  fixed acidity  0.2478
1     0.1379  fixed acidity  0.2832
2     0.2099  fixed acidity  0.2832
3    -0.3061  fixed acidity  0.5841

#====================================================
# ■ Pandas Plot
#    PythonでPandasのPlot機能を使えばデータ加工からグラフ作成までマジでシームレス
#      https://qiita.com/hik0107/items/de5785f680096df93efa
#    matplotlib (+ pandas) によるデータ可視化の方法 (3)
#      https://qiita.com/ynakayama/items/9979258ac68cb669757a

# 配列の視覚化(ヒートマップ的な)

import numpy  as np
import matplotlib.pyplot  as plt
%matplotlib inline

n = 5
img  = np.zeros((n,n,3),'float32') # プロット用配列
c0 = 1/(n*n)
c  = 0.0
for y in range(n):
    for x in range(n):
        c += c0
        img[y, x, :] = c
print(img[:, :, 0])

fig, ax = plt.subplots(figsize=(5,5))
ax.grid(False)
ax.imshow(img)

[[0.04 0.08 0.12 0.16 0.2 ]
 [0.24 0.28 0.32 0.36 0.4 ]
 [0.44 0.48 0.52 0.56 0.6 ]
 [0.64 0.68 0.72 0.76 0.8 ]
 [0.84 0.88 0.92 0.96 1.  ]]

<matplotlib.image.AxesImage at 0x1ebff2f37f0>

# カラーサンプル

# List of named colors
# https://matplotlib.org/stable/gallery/color/named_colors.html

from matplotlib.patches import Rectangle
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors


def plot_colortable(colors, title, sort_colors=True, emptycols=0):

    cell_width = 212
    cell_height = 22
    swatch_width = 48
    margin = 12
    topmargin = 40

    # Sort colors by hue, saturation, value and name.
    if sort_colors is True:
        by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgb(color))),
                         name)
                        for name, color in colors.items())
        names = [name for hsv, name in by_hsv]
    else:
        names = list(colors)

    n = len(names)
    ncols = 4 - emptycols
    nrows = n // ncols + int(n % ncols > 0)

    width = cell_width * 4 + 2 * margin
    height = cell_height * nrows + margin + topmargin
    dpi = 72

    fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi)
    fig.subplots_adjust(margin/width, margin/height,
                        (width-margin)/width, (height-topmargin)/height)
    ax.set_xlim(0, cell_width * 4)
    ax.set_ylim(cell_height * (nrows-0.5), -cell_height/2.)
    ax.yaxis.set_visible(False)
    ax.xaxis.set_visible(False)
    ax.set_axis_off()
    ax.set_title(title, fontsize=24, loc="left", pad=10)

    
    print(f'len:{len(names)}')
    for i, name in enumerate(names):
        #print(i, colors[name], name)
        row = i % nrows
        col = i // nrows
        y = row * cell_height

        swatch_start_x = cell_width * col
        text_pos_x = cell_width * col + swatch_width + 7

        ax.text(text_pos_x, y, 
                str(colors[name])+' '+name, 
                fontsize=14,
                horizontalalignment='left',
                verticalalignment='center')

        ax.add_patch(
            Rectangle(xy=(swatch_start_x, y-9), width=swatch_width,
                      height=18, facecolor=colors[name], edgecolor='0.7')
        )
    
    plt.show()
    return fig


def plot_hex_colors(hex_colors, title, emptycols=0):

    cell_width = 212
    cell_height = 22
    swatch_width = 48
    margin = 12
    topmargin = 40

    # Sort colors by hue, saturation, value and name.
    n = len(hex_colors)
    ncols = 4 - emptycols
    nrows = n // ncols + int(n % ncols > 0)

    width = cell_width * 4 + 2 * margin
    height = cell_height * nrows + margin + topmargin
    dpi = 72

    fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi)
    fig.subplots_adjust(margin/width, margin/height,
                        (width-margin)/width, (height-topmargin)/height)
    ax.set_xlim(0, cell_width * 4)
    ax.set_ylim(cell_height * (nrows-0.5), -cell_height/2.)
    ax.yaxis.set_visible(False)
    ax.xaxis.set_visible(False)
    ax.set_axis_off()
    ax.set_title(title, fontsize=24, loc="left", pad=10)

    print(f'len:{len(hex_colors)}')
    for i in range(len(hex_colors)):
        #print(f'{hex_colors[i]}')
        row = i % nrows
        col = i // nrows
        y = row * cell_height

        swatch_start_x = cell_width * col
        text_pos_x = cell_width * col + swatch_width + 7

        ax.text(text_pos_x, y, hex_colors[i], fontsize=14,
                horizontalalignment='left',
                verticalalignment='center')

        ax.add_patch(
            Rectangle(xy=(swatch_start_x, y-9), width=swatch_width,
                      height=18, facecolor=hex_colors[i], edgecolor='0.7')
        )
    
    plt.show()
    return fig


hex_colors = [
#    red                  yellow              green               cyan                 blue               magenta     
    '#FF0000','#FF7F00','#FFFF00','#7FFF00','#00FF00','#00FF7F','#00FFFF','#007FFF','#0000FF','#7F00FF','#FF00FF','#FF009F',
    '#8F0000','#8F5F00','#8F8F00','#5F8F00','#008F00','#008F5F','#008F8F','#005F8F','#00008F','#5F008F','#8F008F','#8F005F',
    '#FF7F7F','#FFAF4F','#FFFF7F','#AFFF7F','#7FFF7F','#7FFFAF','#7FFFFF','#7FAFFF','#7F7FFF','#AF7FFF','#FF7FFF','#FF7FCF',
]
plot_hex_colors(hex_colors, "Hex Colors", emptycols=1)

color_names = ['red', 'darkorange', 'gold', 'olivedrab', 'forestgreen','green', 'darkcyan',
               'blue', 'dodgerblue',
               'blueviolet', 'purple', 'darkviolet','mediumvioletred']
plot_colortable({c:mcolors.CSS4_COLORS[c] for c in color_names}, 
                "Original Color Seq.", sort_colors=False, emptycols=1)

color_names = ['pink', 'orange', 'yellow', 'yellowgreen', 'limegreen','green', 'lightseagreen',
               'deepskyblue', 'turquoise',  
               'mediumpurple', 'magenta', 'violet','orchid']
plot_colortable({c:mcolors.CSS4_COLORS[c] for c in color_names}, 
                "Original Color Seq.", sort_colors=False, emptycols=1)


plot_colortable(mcolors.BASE_COLORS, "Base Colors", sort_colors=False, emptycols=1)
plot_colortable(mcolors.TABLEAU_COLORS, "Tableau Palette", sort_colors=False, emptycols=2)

plot_colortable(mcolors.CSS4_COLORS, "CSS Colors")
plot_colortable(mcolors.cnames, "cnames Colors", sort_colors=False, emptycols=1)



# Optionally plot the XKCD colors (Caution: will produce large figure)
#xkcd_fig = plot_colortable(mcolors.XKCD_COLORS, "XKCD Colors")
#xkcd_fig.savefig("XKCD_Colors.png")

plt.show()

len:36

len:13

len:13

len:8

len:10

len:148

len:148

## 分散が異なる2群のt検定
##   p値が0.05以下の場合には、優位水準5％で2群が等しいという仮説が棄却される

import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from scipy import stats

%matplotlib inline

np.random.seed(0)
data1 = stats.norm.rvs(loc=0, scale=1, size=100)
np.random.seed(0)
data2 = stats.norm.rvs(loc=2, scale=3, size=100) 

fig, ax = plt.subplots()
sns.histplot(x=data1, binwidth=0.5, color='blue',   label='data1: mean=0, sd=1', kde=True)
sns.histplot(x=data2, binwidth=0.5, color='orange', label='data2: mean=2, sd=3', kde=True)
ax.set_title('Two Normal distribution random numbers Histogram')
ax.legend()
ax.set_xlabel('X')
plt.show()

stats.ttest_ind(data1, data2, equal_var=False)

Ttest_indResult(statistic=-6.617058867031349, pvalue=1.0610598243546533e-09)

#============================================
# ネットワーク図
# ◆ NetworkX
#
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import random
import string
%matplotlib inline
random.seed(0)
n = 10
adj = [random.sample(range(n), 2) for _ in range(n*3)]
print(adj)

# Instantiate a Graph object form the list of edges as argument
g = nx.Graph(adj)

plt.figure(figsize=(4,4));
node_size  = (np.array(g.nodes())+ 5)*30
node_color = (np.array(g.nodes())+10)*10
labels = dict(zip(g.nodes(), [string.ascii_uppercase[i] for i in g.nodes()]))
print(g.nodes(), node_size, node_color, labels)

nx.draw(g, cmap=plt.cm.winter, alpha=.8, font_size=20, width=.5,
        node_size=node_size, node_color=node_color, labels=labels);

plt.axis('off');
plt.show()

[[6, 9], [0, 4], [8, 7], [6, 4], [7, 5], [9, 3], [8, 2], [4, 2], [1, 4], [8, 2], [4, 1], [1, 5], [7, 8], [1, 5], [6, 5], [9, 3], [8, 7], [7, 8], [4, 0], [8, 0], [1, 6], [0, 7], [5, 3], [5, 1], [3, 9], [3, 2], [8, 7], [1, 9], [5, 8], [7, 1]]
[6, 9, 0, 4, 8, 7, 5, 3, 2, 1] [330 420 150 270 390 360 300 240 210 180] [160 190 100 140 180 170 150 130 120 110] {6: 'G', 9: 'J', 0: 'A', 4: 'E', 8: 'I', 7: 'H', 5: 'F', 3: 'D', 2: 'C', 1: 'B'}